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In automated driving, the states of the target vehicle which could be used to characterize the vehicle behaviors are usually required for the host vehicle control. However, some key states of target vehicle are difficult to measure directly and accurately in all driving situations. In addition to this, it is hard to get the accurate parameters of the target vehicle to establish vehicle dynamics-based method which is commonly adopted to estimate the vehicle states. To address these problems, this paper investigated a novel methodology for estimating the states of target vehicle using the information gathered by several host vehicle sensors such as the camera, light detection and ranging (LiDAR) and the radar. A vehicle kinematic model based on Serret-Frenet equation was constructed, which could be used to interpret the target vehicle lateral motion.

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency.